Abstract
Social tagging systems allow people to classify Web resources by using a set of freely chosen terms commonly called tags. However, by shifting the classification task from a set of experts to a larger and untrained set of people, the results of the classification are not accurate. The lack of control and guidelines generates noisy tags (i.e. tags without a clear semantic) which lower the precision of the user generated classifications. In order to face this limitation several tools have been proposed in the literature for suggesting to the users tags which properly describe a given resource. On the other hand we propose to suggest n-grams (named keyphrases) by following the idea that sequences of two/three terms can better face potential ambiguities. More specifically, in this work, we identify a set of features which characterize n-grams adequate for describing meaningful aspects reported in the Web pages. By means of these features, we developed a mechanism which can support people when classifying Web pages by automatically suggesting meaningful keyphrases.
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Ferrara, F., Tasso, C. (2013). Extracting Keyphrases from Web Pages. In: Agosti, M., Esposito, F., Ferilli, S., Ferro, N. (eds) Digital Libraries and Archives. IRCDL 2012. Communications in Computer and Information Science, vol 354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35834-0_11
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DOI: https://doi.org/10.1007/978-3-642-35834-0_11
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